How do I install and run the MCP server?

Install using uv (recommended) with the command: uvx mcp-server-appwrite [args], or install via pip with: pip install mcp-server-appwrite and run with: python -m mcp_server_appwrite [args].

How can I enable additional API tools for the MCP server?

Use the command-line arguments to enable API tools: --tables-db, --users, --teams, --storage, --functions, --messaging, --locale, --avatars, --sites, --all, and --databases.

How do I configure Claude Desktop to use the Appwrite MCP server?

In Claude Desktop Settings > Developer > Edit Config, add the JSON config snippet shown in the documentation, which includes mcpServers.appwrite with command: 'uv' and env variables APPWRITE_PROJECT_ID, APPWRITE_API_KEY, and optionally APPWRITE_ENDPOINT.

How do I configure VS Code to use the Appwrite MCP server?

In VS Code, open MCP: Open User Configuration and add the Appwrite MCP server configuration under 'servers', including command 'uvx', args ['mcp-server-appwrite', '--users'], and the Appwrite environment variables.

Back to Directory

Appwrite MCP server

Featured

mcp appwrite backend api integration cli ai-assist model-context-protocol

View Source Visit Website

Appwrite MCP server Details

Appwrite MCP server is a Model Context Protocol server that enables AI models to interact with Appwrite’s backend. It provides a curated set of MCP tools to manage databases, users, functions, teams, and more within your Appwrite project, enabling powerful AI-assisted workflows and natural-language interactions with your backend. The server ships with the Databases tools enabled by default to keep prompts within context limits and can be extended by enabling additional APIs via command-line flags. This makes it easier to build AI-powered applications that leverage Appwrite APIs securely and efficiently.

Use Case

This MCP server enables AI-powered applications to interact with Appwrite APIs through natural language prompts. By exposing a controlled set of APIs (tools) to the MCP, developers can build assistants, copilots, or automation that can read/write databases, manage users and teams, handle storage, and more, all within the Appwrite ecosystem. The server supports enabling additional tools/APIs via flags, and various IDE integrations (Claude Desktop, Cursor, Windsurf, VS Code) provide seamless configuration and usage patterns.

Usage examples from the documentation:

Start with uvx (or uv) to run the MCP server:

uvx mcp-server-appwrite [args]

Install via pip and run with Python:

pip install mcp-server-appwrite

python -m mcp_server_appwrite [args]

Enable tools via command-line flags (example from the docs):

--tables-db  Enables the TablesDB API
  --users      Enables the Users API
  --teams      Enables the Teams API
  --storage    Enables the Storage API
  --functions  Enables the Functions API
  --messaging  Enables the Messaging API
  --locale     Enables the Locale API
  --avatars    Enables the Avatars API
  --sites      Enables the Sites API
  --all        Enables all Appwrite APIs
  --databases  Enables the Legacy Databases API

Available Tools (11)

Examples & Tutorials

Claude Desktop configuration (real JSON snippet):

{
  "mcpServers": {
    "appwrite": {
      "command": "uv",
      "args": [\\
        "mcp-server-appwrite"\\
      ],
      "env": {
        "APPWRITE_PROJECT_ID": "<YOUR_PROJECT_ID>",
        "APPWRITE_API_KEY": "<YOUR_API_KEY>",
        "APPWRITE_ENDPOINT": "https://<REGION>.cloud.appwrite.io/v1" // Optional
      }
    }
  }
}

Cursor integration command examples:

MacOS

env APPWRITE_API_KEY=your-api-key env APPWRITE_PROJECT_ID=your-project-id uvx mcp-server-appwrite

Windows

cmd /c SET APPWRITE_PROJECT_ID=your-project-id && SET APPWRITE_API_KEY=your-api-key && uvx mcp-server-appwrite

Windsurf integration (config snippet):

{
  "mcpServers": {
    "appwrite": {
      "command": "uvx",
      "args": [\\
        "mcp-server-appwrite"\\
      ],
      "env": {
        "APPWRITE_PROJECT_ID": "<YOUR_PROJECT_ID>",
        "APPWRITE_API_KEY": "<YOUR_API_KEY>",
        "APPWRITE_ENDPOINT": "https://<REGION>.cloud.appwrite.io/v1" // Optional
      }
    }
  }
}

VS Code configuration:

{
  "servers": {
    "appwrite": {
      "command": "uvx",
      "args": ["mcp-server-appwrite", "--users"],
      "env": {
        "APPWRITE_PROJECT_ID": "<YOUR_PROJECT_ID>",
        "APPWRITE_API_KEY": "<YOUR_API_KEY>",
        "APPWRITE_ENDPOINT": "https://<REGION>.cloud.appwrite.io/v1"
      }
    }
  }
}

Local development and run commands from the docs:

git clone https://github.com/appwrite/mcp.git

curl -LsSf https://astral.sh/uv/install.sh | sh  # Linux/MacOS (install uv)

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"  # Windows (PowerShell, install uv)

uv venv

source .venv/bin/activate

uv run -v --directory ./ mcp-server-appwrite

Claude Desktop final usage example (CLI config):

{
  "mcpServers": {
    "appwrite": {
      "command": "uv",
      "args": [
        "mcp-server-appwrite"
      ],
      "env": {
        "APPWRITE_PROJECT_ID": "<YOUR_PROJECT_ID>",
        "APPWRITE_API_KEY": "<YOUR_API_KEY>",
        "APPWRITE_ENDPOINT": "https://<REGION>.cloud.appwrite.io/v1" // Optional
      }
    }
  }
}

Installation Guide

Using uv (recommended):

uvx mcp-server-appwrite [args]

Using pip:

pip install mcp-server-appwrite

Then run the server using:

python -m mcp_server_appwrite [args]

Integration Guides

Frequently Asked Questions

Is this your MCP?

Claim ownership and get verified badge

Repository Stats

Compare Alternatives

ChainAware Behavioural Prediction MCP

Sequential Thinking MCP Server

Compare →

Similar MCP Tools

9 related tools

Graphiti MCP Server

Graphiti MCP Server is an experimental implementation that exposes Graphiti's real-time, temporally-aware knowledge graph capabilities through the MCP (Model Context Protocol) interface. It enables AI agents and MCP clients to interact with Graphiti's knowledge graph for structured extraction, reasoning, and memory across conversations, documents, and enterprise data. The server supports multiple backends (FalkorDB by default and Neo4j), a variety of LLM providers (OpenAI, Anthropic, Gemini, Groq, Azure OpenAI), and multiple embedder options, all accessible via an HTTP MCP endpoint at /mcp/ for broad client compatibility. It also includes queue-based asynchronous episode processing, rich entity types for structured data, and flexible configuration through config.yaml, environment variables, or CLI arguments.

Context7 MCP Server

Context7 MCP Server delivers up-to-date, code-first documentation and examples for LLMs and AI code editors by pulling content directly from the source. It supports multiple MCP clients and exposes tools that help you resolve library IDs and retrieve library documentation, ensuring prompts use current APIs and usage patterns. The repository provides installation and integration guides for Cursor, Claude Code, Opencode, and other clients, along with practical configuration samples and OAuth options for remote HTTP connections. This MCP server is designed to keep prompts in sync with the latest library docs, reducing hallucinations and outdated code snippets.

TrendRadar MCP

TrendRadar MCP is an AI-driven Model Context Protocol (MCP) based analysis server that exposes a suite of specialized tools for cross-platform news analysis, trend tracking, and intelligent push notifications. It integrates with TrendRadar’s multi-platform data aggregation (RSS and trending topics) and provides advanced AI-powered insights, sentiment analysis, and cross-platform correlation. The MCP server enables developers to query, analyze, and compare news across platforms using a consistent toolset, with ongoing updates that expand capabilities such as RSS querying, date parsing, and multi-date trend analysis. This documentation references the MCP module updates, tool additions, and architecture changes that enhance extensibility, cross-platform data handling, and AI-assisted reporting.

ChainAware Behavioural Prediction MCP

The ChainAware Behavioural Prediction MCP is an MCP-based server that provides AI-powered tools to analyze wallet behaviour prediction, fraud detection, and rug pull prediction. Designed for Web3 security and DeFi analytics, it enables developers and platforms to integrate risk assessment, predictive wallet behavior insights, and rug-pull detection through MCP-compatible clients. The server exposes three specialized tools and uses Server-Sent Events (SSE) for real-time responses, helping safeguard DeFi users, monitor liquidity risks, and score wallet or contract trustworthiness. Access to production endpoints is API-key gated, reflecting a private backend architecture that supports secure, scalable risk analytics across wallets, contracts, and pools.

Playwright MCP

Playwright MCP server. A Model Context Protocol (MCP) server that provides browser automation capabilities using Playwright. This server enables large language models (LLMs) to interact with web pages through structured accessibility snapshots, bypassing the need for screenshots or visually-tuned models. The server is designed to be fast, lightweight, and deterministic, offering LLM-friendly tooling and a rich set of browser automation capabilities via MCP tools. It supports standalone operation, containerized deployments, and integration with a variety of MCP clients (Claude Desktop, VS Code, Copilot, Cursor, Goose, Windsurf, and others).

Sequential Thinking MCP Server

Sequential Thinking MCP Server provides a dedicated MCP tool that guides problem-solving through a structured, step-by-step thinking process. It supports dynamic adjustment of the number of thoughts and allows revision and branching within a controlled workflow, making it ideal for complex analysis and solution hypothesis development. This server is designed to register a single tool, sequential_thinking, and is integrated with common MCP deployment methods (NPX, Docker) as well as editor integrations like Claude Desktop and VS Code for quick setup. The documentation provides exact configuration snippets, usage patterns, and building instructions to help you deploy and use the MCP server effectively, including Codex CLI, NPX, and Docker installation examples.

N8N MCP Server

An MCP (Model Context Protocol) server designed to integrate Claude Desktop, Claude Code, Windsurf, and Cursor with n8n workflows. This MCP enables users to build, test, and orchestrate complex workflows by exposing a set of tools that bridge Claude’s capabilities with n8n’s automation platform. The project emphasizes robust trigger handling, multi-tenant readiness, and progressive documentation to help developers understand how tools map to real-world workflow tasks. It also outlines future tooling integration points (such as getNodeEssentials and getNodeInfo) to further enhance node-structure awareness within MCP-powered automations.

Hugging Face MCP Server

Hugging Face Official MCP Server connects your large language models (LLMs) to the Hugging Face Hub and thousands of Gradio AI Applications, enabling seamless MCP (Model Context Protocol) integration across multiple transports. It supports STDIO, SSE (to be deprecated but still commonly deployed), StreamableHTTP, and StreamableHTTPJson, with the Web Application allowing dynamic tool management and status updates. This MCP server is designed to be run locally or in Docker, and it provides integrations with Claude Desktop, Claude Code, Gemini CLI (and its extension), VSCode, and Cursor, making it easy to configure and manage MCP-enabled tools and endpoints. Tools such as hf_doc_search and hf_doc_fetch can be enabled to enhance document discovery, and an optional Authenticate tool can be included to handle OAuth challenges when called.

Shadcn UI MCP Server v4

Shadcn UI v4 MCP Server is an advanced MCP (Model Context Protocol) server designed to give AI assistants comprehensive access to shadcn/ui v4 components, blocks, demos, and metadata. It enables multi-framework support (React, Svelte, Vue, and React Native) with fast, cache-friendly access to component source code, demos, and directory structures, empowering AI-driven development workflows. The project emphasizes production-readiness with Docker Compose, SSE transport for multi-client deployments, and smart caching to optimize GitHub API usage while providing rich metadata and usage patterns for rapid prototyping and learning across frameworks.

Appwrite MCP server

Appwrite MCP server Details

Use Case

Available Tools (11)

tables-db

users

teams

storage

functions

messaging

locale

avatars

sites

all

databases

Examples & Tutorials

Installation Guide

Integration Guides

Claude Desktop

VS Code

Frequently Asked Questions

What prerequisites are required before using the MCP server?

How do I install and run the MCP server?

How can I enable additional API tools for the MCP server?

How do I configure Claude Desktop to use the Appwrite MCP server?

How do I configure VS Code to use the Appwrite MCP server?

Is this your MCP?

Compare Alternatives

Similar MCP Tools

Graphiti MCP Server

Context7 MCP Server

TrendRadar MCP

ChainAware Behavioural Prediction MCP

Playwright MCP

Sequential Thinking MCP Server

N8N MCP Server

Hugging Face MCP Server

Shadcn UI MCP Server v4